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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

An automated algorithm for the detection of cortical interruptions and its underlying loss of trabecular bone; a

M Peters^1,2,3, J de Jong^4,5,6, A Scharmga^4,7,5

¹Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Centre, P.O. Box 5800, NL-6202, Maastricht, AZ, the Netherlands. michiel.peters@maastrichtuniversity.nl.

BMC Medical Imaging

|May 17, 2018

Abstract

Background:

We developed a semi-automated algorithm that detects cortical interruptions in finger joints using high-resolution peripheral quantitative computed tomography (HR-pQCT), and extended it with trabecular void volume measurement. In this study we tested the reproducibility of the algorithm using scan/re-scan data.

Methods:

Second and third metacarpophalangeal joints of 21 subjects (mean age 49 (SD 11) years, 17 early rheumatoid arthritis and 4 undifferentiated arthritis, all diagnosed < 1 year ago) were imaged twice by HR-pQCT on the same day with repositioning between scans. The images were analyzed twice by one operator (OP1) and once by an additional operator (OP2), who independently corrected the bone contours when necessary. The number, surface and volume of interruptions per joint were obtained. Intra- and inter-operator reliability and intra-operator reproducibility were determined by intra-class correlation coefficients (ICC). Intra-operator reproducibility errors were determined as the least significant change (LSC_SD).

Results:

Per joint, the mean number of interruptions was 3.1 (SD 3.6), mean interruption surface 4.2 (SD 7.2) mm², and mean interruption volume 3.5 (SD 10.6) mm³ for OP1. Intra- and inter-operator reliability was excellent for the cortical interruption parameters (ICC ≥0.91), except good for the inter-operator reliability of the interruption surface (ICC = 0.70). The LSC_SD per joint was 4.2 for the number of interruptions, 5.8 mm² for interruption surface, and 3.2 mm³ for interruption volume.

Conclusions:

The algorithm was highly reproducible in the detection of cortical interruptions and their volume. Based on the LSC findings, the potential value of this algorithm for monitoring structural damage in the joints in early arthritis patients needs to be tested in clinical studies.

Keywords:

Bone micro-architecture Cortical interruptions High resolution peripheral quantitative computed tomography Image processing Precision Rheumatoid arthritis

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